2,760 research outputs found

    Generating axial magnetic fields via two plasmon decay driven by a twisted laser

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    We propose a new way of axial magnetic fields generation in a non-relativistic laser intensity regime by using a twisted light carrying orbital angular momentum (OAM) to stimulate two-plasmon decay (TPD) in a plasma. The growth of TPD driven by an OAM light in a Laguerre-Gauss (LG) mode is investigated through three dimensional fluid simulations and theory. A theory based on the assumption that the electron plasma waves (EPWs) are locally driven by a number of local plane-wave lasers predicts the maximum growth rate proportional to the peak amplitude of the pump laser field and is verified by the simulations. The OAM conservation during its transportation from the laser to the TPD daughter EWPs is shown by both the theory and the simulations. The theory predicts generation of ~40T axial magnetic fields through the OAM absorption via TPD, which has perspective applications in the field of high energy density physics.Comment: 6 pages, 3 figures

    Wind power output prediction: a comparative study of extreme learning machine

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    This study aims to propose a wind power prediction method that achieves high accuracy in order to minimize the impact of wind power on the power system and reduce scheduling difficulties in systems incorporating wind power. The importance of developing renewable energy has been recognized by society due to the increasing severity of the energy crisis. Wind energy offers advantages such as efficiency, cleanliness, and ease of development. However, the random nature of wind energy poses challenges to power systems and complicates the scheduling process. Therefore, accurate wind power prediction is of utmost importance. A wind power prediction model was constructed based on an improved tunicate swarm algorithm–extreme learning machine (ITSA-ELM). The improved tunicate swarm algorithm (ITSA) optimizes the random parameters of extreme learning machine (ELM), resulting in the best prediction performance. ITSA is an enhancement of the tunicate swarm algorithm (TSA), which introduces a reverse learning mechanism, a non-linear self-learning factor, and a Cauchy mutation strategy to address the drawbacks of poor convergence and susceptibility to local optima in TSA. Two different scenarios were used to verify the effectiveness of ITSA-ELM. The results showed that ITSA-ELM has a decrease of 1.20% and 21.67% in MAPE, compared with TSA-ELM, in May and December, respectively. This study has significant implications for promoting the development of renewable energy and reducing scheduling difficulties in power systems

    The clustering of galaxies in the completed SDSS-III Baryon Oscillation Spectroscopic Survey: a tomographic measurement of structure growth and expansion rate from anisotropic galaxy clustering in Fourier space

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    We perform a tomographic structure growth and expansion rate analysis using the monopole, quadrupole and hexadecapole of the redshift-space galaxy power spectrum derived from the Sloan Digital Sky Survey (SDSS-III) Baryon Oscillation Spectroscopic Survey (BOSS) Data Release 12 combined sample, which covers the redshift range of 0.20<z<0.750.20<z<0.75. By allowing for overlap between neighbouring redshift slices in order to extract information on the light-cone, we successfully obtain joint BAO and RSD constraints with a precision of 23%2-3\% for DAD_A, 310%3-10\% for HH and 912%9-12\% for fσ8f\sigma_8 with a redshift resolution of Δz0.04\Delta z\sim0.04. Our measurement is consistent with that presented in arXiv:1709.05173, where the analysis is performed in configuration space. We apply our measurement to constrain the f(R)f(R) gravity model, and find that the 95\% CL upper limit of log10B0{\rm log_{10}}B_0 can be reduced by 11\% by our tomographic BAO and RSD measurement.Comment: 9 pages, 5 figures; Tomographic BAO and RSD measurement derived from this work is available at https://github.com/Alice-Zheng/RSD-data; version accepted to MNRA
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